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start implement ICP for ssgplvm

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Zhenwen Dai 2015-05-18 16:36:39 +01:00
parent d01545c92b
commit 7188e92efb
3 changed files with 84 additions and 6 deletions
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6
GPy/core/parameterization/variational.py
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@ -179,15 +179,15 @@ class SpikeAndSlabPosterior(VariationalPosterior):
n.parameters[dc['variance']._parent_index_] = dc['variance']
n.parameters[dc['binary_prob']._parent_index_] = dc['binary_prob']
n._gradient_array_ = None
oversize = self.size - self.mean.size - self.variance.size
n.size = n.mean.size + n.variance.size + oversize
oversize = self.size - self.mean.size - self.variance.size - self.gamma.size
n.size = n.mean.size + n.variance.size + n.gamma.size + oversize
n.ndim = n.mean.ndim
n.shape = n.mean.shape
n.num_data = n.mean.shape[0]
n.input_dim = n.mean.shape[1] if n.ndim != 1 else 1
return n
else:
return super(VariationalPrior, self).__getitem__(s)
return super(SpikeAndSlabPosterior, self).__getitem__(s)
def plot(self, *args, **kwargs):
"""

2
GPy/examples/dimensionality_reduction.py
View file

@ -359,7 +359,7 @@ def ssgplvm_simulation(optimize=True, verbose=1,
if optimize:
print("Optimizing model:")
m.optimize('scg', messages=verbose, max_iters=max_iters,
m.optimize('bfgs', messages=verbose, max_iters=max_iters,
gtol=.05)
if plot:
m.X.plot("SSGPLVM Latent Space 1D")

82
GPy/models/ss_gplvm.py
View file

@ -5,11 +5,91 @@ import numpy as np
from ..core.sparse_gp_mpi import SparseGP_MPI
from .. import kern
from ..core.parameterization import Param
from ..likelihoods import Gaussian
from ..core.parameterization.variational import SpikeAndSlabPrior, SpikeAndSlabPosterior
from ..inference.latent_function_inference.var_dtc_parallel import update_gradients, VarDTC_minibatch
from ..kern._src.psi_comp.ssrbf_psi_gpucomp import PSICOMP_SSRBF_GPU
class IBPPosterior(SpikeAndSlabPosterior):
'''
The SpikeAndSlab distribution for variational approximations.
'''
def __init__(self, means, variances, binary_prob, tau, name='latent space'):
"""
binary_prob : the probability of the distribution on the slab part.
"""
from ..core.parameterization.transformations import Logexp, __fixed__
super(IBPPosterior, self).__init__(means, variances, name)
self.tau = Param("tau",SpikeAndSlabPosterior, Logexp)
self.link_parameter(self.tau)
def set_gradients(self, grad):
self.mean.gradient, self.variance.gradient, self.gamma.gradient, self.tau.gradient = grad
def __getitem__(self, s):
if isinstance(s, (int, slice, tuple, list, np.ndarray)):
import copy
n = self.__new__(self.__class__, self.name)
dc = self.__dict__.copy()
dc['mean'] = self.mean[s]
dc['variance'] = self.variance[s]
dc['binary_prob'] = self.binary_prob[s]
dc['tau'] = self.tau[s]
dc['parameters'] = copy.copy(self.parameters)
n.__dict__.update(dc)
n.parameters[dc['mean']._parent_index_] = dc['mean']
n.parameters[dc['variance']._parent_index_] = dc['variance']
n.parameters[dc['binary_prob']._parent_index_] = dc['binary_prob']
n.parameters[dc['tau']._parent_index_] = dc['tau']
n._gradient_array_ = None
oversize = self.size - self.mean.size - self.variance.size - self.gamma.size - self.tau.size
n.size = n.mean.size + n.variance.size + n.gamma.size+ n.tau.size + oversize
n.ndim = n.mean.ndim
n.shape = n.mean.shape
n.num_data = n.mean.shape[0]
n.input_dim = n.mean.shape[1] if n.ndim != 1 else 1
return n
else:
return super(IBPPosterior, self).__getitem__(s)
class IBPPrior(SpikeAndSlabPrior):
def __init__(self, input_dim, alpha =2., name='IBPPrior', **kw):
super(SpikeAndSlabPrior, self).__init__(name=name, **kw)
from ..core.parameterization.transformations import Logexp, __fixed__
self.input_dim = input_dim
self.alpha = Param('alpha', alpha, __fixed__)
self.link_parameter(self.alpha)
def KL_divergence(self, variational_posterior):
mu, S, gamma, tau = variational_posterior.mean.values, variational_posterior.variance.values, variational_posterior.gamma.values, variational_posterior.tau.values
var_mean = np.square(mu)/self.variance
var_S = (S/self.variance - np.log(S))
part1 = (gamma* (np.log(self.variance)-1. +var_mean + var_S)).sum()/2.
ad = self.alpha/self.input_dim
from scipy.special import betaln,digamma
part2 = (gamma*np.log(gamma)).sum() + ((1.-gamma)*np.log(1.-gamma)).sum() + betaln(ad,1.)*self.input_dim \
-betaln(tau[:,0], tau[:,1]).sum() + ((tau[:,0]-gamma-ad)*digamma(tau[:,0])).sum() + \
((tau[:,1]+gamma-2.)*digamma(tau[:,1])).sum() + ((2.+ad-tau[:,0]-tau[:,1])*digamma(tau.sum(axis=1))).sum()
return part1+part2
def update_gradients_KL(self, variational_posterior):
mu, S, gamma, tau = variational_posterior.mean.values, variational_posterior.variance.values, variational_posterior.gamma.values, variational_posterior.tau.values
variational_posterior.binary_prob.gradient -= ((np.square(mu)+S)/self.variance-np.log(S)+np.log(self.variance)-1.)/2.
mu.gradient -= gamma*mu/self.variance
S.gradient -= (1./self.variance - 1./S) * gamma /2.
if self.learnPi:
if len(self.pi)==1:
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum()
elif len(self.pi.shape)==1:
self.pi.gradient = (gamma/self.pi - (1.-gamma)/(1.-self.pi)).sum(axis=0)
else:
self.pi[idx].gradient = (gamma/self.pi[idx] - (1.-gamma)/(1.-self.pi[idx]))
class SSGPLVM(SparseGP_MPI):
"""
Spike-and-Slab Gaussian Process Latent Variable Model
@ -69,8 +149,6 @@ class SSGPLVM(SparseGP_MPI):
X = SpikeAndSlabPosterior(X, X_variance, gamma)
super(SSGPLVM,self).__init__(X, Y, Z, kernel, likelihood, variational_prior=self.variational_prior, inference_method=inference_method, name=name, mpi_comm=mpi_comm, normalizer=normalizer, **kwargs)
# self.X.unfix()
# self.X.variance.constrain_positive()
self.link_parameter(self.X, index=0)
if self.group_spike: